Bennett Indart on NTT DATA’s Digital Transformation Initiatives

This is Techstrong tv. Hi everyone. Welcome back here to Techstrong tv.

Well, I got, I've been looking forward to this session here 'cause it's, it's a little sports thing going on and luck. We all are. But, um, before we jump into it, let me lay the groundwork here and introduce you to Bennett in Dart Bennett is the President of Smart World Solutions at NTT Data Bennett, welcome and thanks for joining us here on Techstrong tv.

Ellen, thanks for having me. Look forward to the discussion. All right, so Bennett, we were talking off camera.

You've got a little bit of a sports background and, um, let's see. You know, I always like to let our audience get a flavor for who we're talking to you. Let's hear a little bit about your story.

If you, I'm not embarrassing you. I apologize. Adam Law.

I, uh, so I, I did plenty of, uh, water polo in college, um, at the collegiate level. And D one won a couple of national championships while I was, while I was at, uh, university of California Berkeley. Really?

Oh, but so scary. Yeah. Yeah.

And, uh, but that's sort of taken me into my career of, of technology actually moral non-linear path. But, uh, have, have really, uh, sort of taken the, the, the data and the collection of data as sort of my career path and, and looking at analytics. And I'm really excited about some of the things that are coming out today with AI and all of that.

But, uh, you know, I, I've, I worked with N-T-T-N-T-T data, which, uh, many of you probably aren't familiar with, um, the NTT group, very, very large, a hundred billion dollar telecommunications technology company out of Japan. Um, and a subsidiary of that is NTT Data Company I work for. Um, and in the last year, NTT group, uh, took all of their international business, all of their global business outside of Japan and sort of consolidated that under the NTT data brand.

Uh, and you can imagine the breadth and depth that that brings from everything from being the third largest data center provider in the world to, uh, uh, uh, the third largest, uh, IP backbone provider in the world from a technology standpoint. And then the system integration heritage that was NTT data in 52 countries around the world. So bringing all of that together, really kind of stitches together from goalpost to goalpost the technology lifecycle.

Absolutely. So I didn't really, I knew they had consolidated their outside of Japan businesses. I didn't realize NT data, NTT data was the, uh, container for it all, if you will.

Yeah, yeah. We represent, uh, the, the telecommunications technology, ICT, you know, and consulting business out really outside in Japan today, the global business. Fantastic.

Now, you, as you mentioned, you're specifically with what they call smart world solutions, correct? Yes. And, You know, that could mean different things to different people.

What, what does it mean here that it, Well, initially we started out, um, looking at some really cool ideas about four or five years ago when this explosion of iot kind of hit the market and, uh, and all the data that that represented, you know, from machine learning and, and being able to take that data from the edge, the digital edge today. So, you know, looking at manufacturing floors and, and the machines that are on the manufacturing floor, looking at vibrations, looking at heat, looking at all the different things, the telemetry that's coming off of those. Uh, same thing with, um, um, with stadiums and, and venues that we work in today.

Uh, looking at computer vision and being able to look at crowds and different movements of people transit systems and looking at crowding in the, in the transit system, or just scheduling in general in terms of optimization of those schedules. Um, one of the things that we did after, after, sort of in germinating that idea was, uh, implementing it into the city of Las Vegas and, uh, be, be kind of creating a smart city, um, uh, solution for them, looking at traffic patterns, looking at, um, things that are, um, around safety and security in the city, uh, helping with public services, uh, take, taking all of that data that's available now from computer vision to traffic systems to all of the different places that that data gets collected. And then sitting that down into a, uh, a basically a data architecture that is optimized for analytics and outcomes.

And so being able to look at things like wrong, wrong way, traffic, wrong way, incidents, and, and all of the things that happen around a city, uh, today where citizens are moving in and outta places. Uh, economic development, for example, where do I build, you know, or where do I put my next Starbucks, for example? Um, and then that translated pretty cool into, uh, some of the things that NTT data and NTT does, uh, from a sporting standpoint where we can sort of look test a lot of this out.

And the, uh, NTT IndyCar series is one of the places I I really am excited to talk to you about today. I mean, with sports and the Olympics just finishing up and, and a lot of the technology that's in sports today has really helped enhance the fan experience. Um, for example, um, you know, we look at soccer or football we call it in, in, as they call it, in Europe, right?

And they have now, uh, the, the VR and, and VR has made sway into a lot of other, uh, sporting, uh, events, but specifically in, in, in, uh, soccer where they're looking at off size. And now today, you can use computer vision to actually help be that third referee on the field, right? You have the, the main referee, you have the referee on the silence, then you have the video analytics helping you, uh, understand how, um, how short plays are made.

And then, you know, also controversial sometimes about calling bulls back because of both sides. So it's, it's really helping that, and baseball is the same way with the, the computer vision, all the, all the strike zone and All that. I say do away with the whole playing umpire.

But that's me. I, I, I'll tell you something, where this really strikes out to me is tennis and volley right on, on the Olympics this year that showed it. You've seen it on tennis matches before.

You know, the linesman, you could appeal their calls and, and, and you know, that that video grabbing where it shows the ball, whether it actually hits the line or not, volleyball, and it's says, well, it's pretty clear cut. You can't argue with it anymore. And I, I Argue with that.

I I, I love that kind of feature in there, right? I wish we'd have more of that. Yeah.

So, so imagine taking that, that data that you're getting and, and, and magnifying that across a historical, um, you know, set of experiences. So like, so IndyCar, each IndyCar, um, on the racetrack has about 140 different sensors. And those sensors are in a, in a millisecond, you know, sending data back to the crews to, to look at, you know, how the car's performing.

We decided to take that data as well and give it to the fans in a way of kind of interesting stories that are being told during the race. Who's moving up, who's breaking more, who's, who's getting more g-forces in the, in the, in the corners, which indicates certain types of aggressive movements and things to pass and all of that. And over the last several years, as the title sponsor for the IndyCar series, NTT and the Smart World program has created these analytics that we're giving back to the fans and we're predicting who's gonna win the race.

We're predicting different strategies on pit times and pit pit strategies that's gonna win the race. Um, and then, you know, using that data to, to really kind of bring, uh, more fans into, uh, into the racing, uh, experience. And then we took that same platform and pointed it at the venue itself, and created a, basically a digital twin of, of the Indian for, for example, Indianapolis Motor Speedway largest sporting event, uh, single day sporting event on the planet, uh, on Memorial Day weekend.

350,000 people come to that. It's the second largest city in the state of Indiana on that day. Imagine getting people in and out of that in a very, you know, timely manner, you know, where, where the fans are experienced and have a good experience.

So we, we've taken that same platform, looked at all of the entries, the ingress and the egress, uh, places for people coming in, giving the, the fans a sense of cue times, for example, each gate. So if they walk up to a gate, they scan their ticket, they can see how long it's gonna take them to get into that gate before they show up. 'cause we have it on an app and we're giving that data to them.

And we're also telling them and predicting ahead of time what it's gonna look like in 30 minutes, in an hour so they can start to plan their journey. And then the same thing, uh, we're doing in, uh, concession stands inside the venue. Another venue we're working on is the Washington Nationals.

Every baseball game, uh, today we have, uh, a, you know, an example application for the fans that tell them if they go to a certain concession stand, how long is that gonna take to get, you know, get me through, you know, and get my order. Sure. But the other side of it is, is for the concessionaires too, because the concessionaires now look at how long, how efficient are they, do they have enough employees?

Are they wasting food because they're not preparing the right food for the right time of the game, and so on and so forth. So they're able to do all these analytics on that data. Um, and that's translating into lots of different use cases and other industries as I mentioned earlier.

Sure. Is, but we also, you need to remember, you know, people think of sports is sort of hobbyish, but sports, especially events that you're talking about, 80 baseball games, 82, 81 baseball games and Indian caught certainly net 8,500, these are a billion dollar multi-billion dollar ventures. And so, you know, when you can be smart and, and, you know, be more efficient, the the savings are, are substantial.

That's even before you take the learning that you learn here and apply it yet to other, you know, business scenarios. Um, right. It, it, it really is.

You know, I, I'll tell you something. Uh, so I watch American football, I call it football. And you know, a couple years ago they started with the, uh, I forgot what cloud provider, women's w 84% probability.

They don't make the first down here 16% probability they go to this receiver. And a lot of us, I think watching it always said, ah, that's all right. You know, it's col colon plays.

It wouldn't, but there's something to be said about having the data about yeah. Doing the analytics. And it translates much better into the business of the sport than I think it does into the, what's the chances of, of getting a strike or a home run of this pitch or what happened.

Yeah. You know, if you think about just the example you gave, though, more people are talking about the sport. So in the case of the sport, it's actually a good thing because we do this, I call it let the debate begin, right?

So I'm gonna say, who predicts the winner? If, if I can predict one winner in, in 16 races of IndyCar, I, I'm batting a thousand, I'm feeling great because it's, there's so many variables There is. But what we're doing is we're actually getting, we're the, it's the fan experience and, and bringing, enhanced, you know, bringing more fans to IndyCar, that really is what, what Penske entertainment and IndyCar are after.

And so in, in that sense, it is sort of, okay, I'm gonna, I'm gonna, I'm interested in this because these guys are saying that this is gonna happen, right? But your point about bringing it into business is isn't really, really important. 'cause that, you know, we talk about ai, predictive AI has been around for, for decades, and we've been working in that space for a while.

Um, and the, the places where this type of data, um, you know, edge data to outcomes is, is really important. And one of the things that we started doing from the beginning is looking at the outcomes first, understanding what's the challenge you're trying to solve? And then let's go back and get the data that will help us in that will inform us and help us solve that, that problem or that, that challenge, rather than throwing a bunch of tech at something and saying, okay, let's ask it a bunch of questions.

And then eventually, we'll, we'll figure out what the, what the problem or the answer to the problem is. If, if we're, you know, if we're lucky, we're going very, very bespoke and very focused and purposeful at the, at the problem first, and then going back and finding that, that data that, that we need. We're doing things across insurance today and, and claims and, and helping insurance companies look at better products as they come up with their new, their new product introductions.

Uh, you know, how, how does what they're doing today really fit the nature of the insurance industry and is it changing and, and looking at trends, things like that. We, uh, we did some things in transit, which I think are really interesting. Coming outta Covid, uh, transit agency down in Asia Pacific was looking to bring ridership back and give them a sense for how crowded the train was because of, you know, concerns around, around covid.

So we looked at that and we, we looked at the ridership, we looked at each train platform and station, um, and, and seeing, you know, sort of those trends in this very massive network of, of, uh, trolleys and buses and so on. What was interesting that came out of that was, um, a digital twin of the entire transit system. And so now the operator could actually get a sense for, um, being able to do scenario management around if there's an unplanned event, how do I handle that?

Is there, if there's a, a football or a rugby game that's on this part of the, of the, uh, of the train line, uh, during a particular time of day, um, how do I react to that? And then being able to rerun those in additional manner, um, so that they're ready and be proactive. So really, really supported that.

But it was not the original use case, which was kind of interesting. No, well, but, but that's the great thing about this kind of technology, right? You use it and then you start saying, wait a second.

Now it has application here, it has application there. We duplicate this and make this so much better. Right.

That's why I think we're really just on the cuts. We are. Yeah.

So that, you know, it's gonna be a smart world out there, not just smart indie car sports. Right. O one of the things I, I think it's also interesting, uh, that's really starting to become a very important priority for most companies is, um, sustainability.

Yeah. And, um, you're, you know, you hear about it all the time and, and, and you know, ESG and whether you're on one side of it or the other, it's here. It's not going away.

And one of the things that I think looked at and, and that is, is a lot of companies don't know where to start. And so we're taking the same technology and pointing it at the energy consumption of a facility, or in our case data centers, and understanding how that energy flows through. What kind of type of energy are you, are you consuming?

How does that then convert to a carbon footprint, CO2 E? Um, all of that is done, uh, today is a, is actually an implementation that we've done for our own data centers. Um, and, and that helps them with their reporting.

It also helps them provide their customers a, a version of their own piece of that data center that they're leasing from us or they're using from us. So that becomes what they call scope three emissions and indirect emissions for that particular customer. So we're providing that service back, but that's just the beginning.

I call it sort of step one. Step two is then once I have what I'm doing today from an energy all the way through to the carbon, I understand I made a net zero pledge. I understand when I'm tried to hit that target, am I on track to that?

I can do simple math and, and and trend. And then the third step, which is I think the, the holy grail is back to that digital twin. Now I can start to play around with, I'm using diesel energy, I want to go to clean energy.

Well, what's the cost of doing that in my current footprint of operations and rerun the model with a different energy source and a different emission factor producing a different set of carbon outputs. And that's the real optimization factor that you get from some of this technology. And it starts with the edge.

It starts with the ability to collect the data, whether it's off the grid or reading a meter, or, you know, in continuously following business management system in a facility all the way through to the outcome, which is, oh, I'm gonna miss my net zero targets. I need to think about capital improvements to be able to hit it in the future. Right.

It's a simple scenario. Absolutely. That it great stuff that if, for people who maybe wanna dive deeper, maybe they have their own ideas of something they can do with NTT data or what have you.

What's the best website to go to for this? Zero? com um, is, is Just so main site.

And and what about smart world? Beyond that Smart world would be on there, uh, under innovations, uh, and data and analytics and intelligence. Um, yeah.

So, you know, look, we got a lot of case studies out there. We just released one, um, around a smart rainforest, believe it or not, really where we're going. Um, there is an outfit in Australia that is looking to regenerate part of the oldest rainforest in the world that had, part of it had been cut down for a banana plantation.

And so they're using sensors, they're using soil, they've got a bunch of environmental scientists on board. We are coming in. This is sort of our, our tech for good, you know, um, piece.

We're coming in with the technology and the data analytics to help them begin that regeneration process. They're geotagging every tree. They're looking at soil, water, uh, they're right next to the Great Barrier Reef.

So everything has to be organic, right? So it is a really cool process and I'm really excited about it. It's a long, it's gonna be a long, a long project.

It Reminds me adding a lag from the bar, the movie the Marshal with Matt David, right? We're gonna science the whatever out of it. Um, but it works.

Anyway, Bennett, thanks for coming on. This is real exciting stuff, man. I'm sure there are a lot of people out here who saying, man, I'd like to do that for a living.

Be involved in projects like this. This sounds cool. Um, thank you and thank you to what you guys do over at TT Data and the Smart Road Solutions group there.

We're gonna take a break here on Tech Drunk tv. We'll be back in just a moment.